About The Role

We are looking for an experienced Senior Data Engineer / AWS Data Engineer with 10–12 years of hands-on experience in designing, developing, and maintaining scalable data engineering solutions. The ideal candidate should have strong expertise in AWS data services, Python, PySpark, SQL, ETL, infrastructure as code, and data orchestration.The candidate will be responsible for building robust data pipelines, integrating cloud and on-premises data sources, optimizing data processing workflows, and supporting enterprise-scale data platforms.

Technology Required Expertise

AWS Lambda, Glue, SNS, orchestration and related data services
Python Strong hands-on experience
PySpark Strong hands-on experience
SQL Advanced SQL, query optimization, data transformation
Redshift Development, optimization, data loading
RDS Database development and management
SSIS ETL development and maintenance
SSMS SQL Server development and troubleshooting
Terraform Infrastructure as Code

YAML Configuration/automation

Git Version control and source code management

Podman Containerization

VS Code Development environment

Good to Have

Experience with Connect
Experience with TOLSE / TOLENT
Experience with CI/CD and DevOps practices.
Experience working in Agile/Scrum environments.
Knowledge of cloud security, IAM, monitoring, and logging.

Qualifications

Bachelor's or Master's degree in Computer Science, Information Technology, Engineering , or a related field.
10 -12 years of professional experience in data engineering, ETL, cloud data platforms, or related technologies.
Strong problem-solving and analytical skills.
Excellent communication and collaboration skills.
Ability to work independently as well as within a cross-functional team.

Target Profile: Senior Data Engineer / AWS Data Engineer / Lead Data Engineer with 10 -12 years of experience

Key Responsibilities

Design, develop, and maintain scalable data pipelines and ETL/ELT workflows.
Develop cloud-native data solutions using AWS services, including Lambda, Glue, SNS, and orchestration services.
Build and optimize data processing jobs using Python and PySpark.
Develop complex SQL queries, stored procedures, and data transformations.
Work with Amazon Redshift and Amazon RDS for data storage, processing, and analytics.
Develop and maintain ETL packages and workflows using SSIS.
Use SSMS for database development, troubleshooting, and performance optimization.
Implement infrastructure and configuration using Terraform and YAML.
Work with Git for source code management, branching, merging, and version control.
Containerize and manage development workloads using Podman.
Use VS Code as the primary development environment.
Implement event-driven and automated workflows using AWS services such as Lambda, SNS, and orchestration frameworks.
Monitor, troubleshoot, and optimize data pipelines and AWS workloads.
Ensure data quality, reliability, security, and performance across data platforms.
Collaborate with architects, developers, business analysts, and other stakeholders to deliver data solutions.
Participate in code reviews, technical discussions, and production support activities.